{"id":"W2021305240","doi":"10.1016/j.jvcir.2006.09.001","title":"Grayscale true two-dimensional dictionary-based image compression","year":2006,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossless compression; Computer science; Lossy compression; Image compression; Lossless JPEG; Grayscale; JPEG; Data compression; Artificial intelligence; Color Cell Compression; Data compression ratio; JPEG 2000; Pixel; Computer vision; Texture compression; Compression (physics); Image (mathematics); Image processing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002231988,0.0004629364,0.0004233463,0.0007124406,0.0002398346,0.0006708129,0.000407202,0.0006026949,0.005247043],"category_scores_gemma":[0.001313623,0.0002000936,0.0002443469,0.00117185,0.0003392154,0.0009307478,0.0006461549,0.0006196853,0.001567036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001780809,"about_ca_system_score_gemma":0.0003337643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005999329,"about_ca_topic_score_gemma":0.0009430329,"domain_scores_codex":[0.9997573,0.00003514713,0.00001562659,0.00003104185,0.0001376996,0.00002318539],"domain_scores_gemma":[0.9995716,0.0001038849,0.00002581419,0.0001479292,0.0001344273,0.00001626864],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008522923,0.0001308211,0.0005510753,0.0002711453,0.00004998462,0.0002245567,0.0000892353,0.0179829,0.162327,0.01839828,0.009896131,0.7892267],"study_design_scores_gemma":[0.0001435972,0.0003902356,0.002834789,0.00009279609,0.00007165353,0.002497057,0.0001145972,0.6943635,0.2638716,0.009632741,0.02591395,0.00007347394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05319247,0.001607347,0.9334146,0.0006805266,0.0007446915,0.0001083841,0.0004588321,0.001212144,0.008580995],"genre_scores_gemma":[0.3966257,0.002643108,0.5784013,0.0005820963,0.0003790866,0.0001388137,0.001071274,0.0002089676,0.01994962],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005247043,"threshold_uncertainty_score":0.01755315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519203767192199,"score_gpt":0.3540390804271585,"score_spread":0.3388470427552365,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}